Invoca is an artificial intelligence call tracking platform that helps companies maximize the ROI of phone interactions.
It does that by using artificial intelligence and machine learning (including its AI-powered customer conversations solution Invoca Signal AI) to ingest voice data from phone calls and draw conclusions from it. These insights can then be used to better understand customers across marketing, sales, and customer experience.
For instance, Invoca's AI has been used by large organizations to lower cost per acquisition, save time, and increase the ROI of paid and social media campaigns that generate phone calls.
We spoke with Dee Anna McPherson, CMO at Invoca, to learn more.
In a single sentence or statement, describe your company.
Invoca is an AI-powered call tracking and conversational analytics company that brings the depth of marketing analytics traditionally limited to digital consumer interactions to the world of human-to-human selling.
With Invoca, marketers can use real-time call and conversational analytics to maximize the return of their paid media campaigns in Google and Facebook, and improve the buying experience by enriching customer profiles in Salesforce and Adobe Experience Cloud.
How does your company use artificial intelligence in its products?
Invoca’s conversational analytics product suite empowers marketing, sales, and customer experience teams to obtain new insights into their conversational interactions with customers.
Invoca first introduced conversational analytics features in 2017, and has made numerous improvements over subsequent years. The core of our technology is Signal AI, which ingests vast voice data sets and draws conclusions by detecting intent and word / phrase patterns. Signal AI is able to detect subtleties that humans might miss, giving marketers, sales, and CX professionals access to valuable, actionable insights that would be otherwise unattainable.
What are the primary marketing use cases for your AI-powered solutions?
With Invoca, marketers can automatically measure call-based conversions, associate the conversion to preceding digital touchpoints, and pass that data into systems like Google Ads and Search Ads 360 to adjust keyword bidding strategies and suppress ads for callers who convert over the phone.
Using this data, marketers achieve two key business outcomes: increasing total call-based conversions while making ad spend more efficient.
Invoca customers include leading brands like eHealth, Samsung, PODS, SunTrust Bank, DISH Network, 1-800-GOT-JUNK?, University Hospitals, BBQ Guys, and Mayo Clinic.
Dish Network describes how they use call center data to inform their digital marketing investment in this Think with Google case study:
“We see value in leveraging our call centers to help acquire new customers, even when they may start their purchase cycle digitally. And we know that more than half our new subscribers will interact with us over the phone before signing up. Because phone marketing drives higher conversion rates, we integrated call conversion data into paid search so our digital marketing could work harder to find us better phone leads.”
This approach, combined with Google Smart Bidding, resulted in 15X lift in conversions and a 60% increase in conversion rate.
What makes your AI-powered solution smarter than traditional approaches and products?
Invoca customers use Signal AI to increase marketing effectiveness and efficiency. Customers have lowered cost per acquisition, saved employee time, increased ROI, and transformed paid search results.
University Hospitals was able to save dozens of hours per week using this approach.
“We’ve been able to save 40 hours a week in total for all of those employees,” said Noah Brooks, manager of analytics and strategy at University Hospitals. “They’re now focusing on optimizing their marketing campaigns, developing new creative, and testing new things.”
Are there any minimum requirements for marketers to get value out of your AI-powered technology? (e.g. data, list size, etc.)
Marketers must replace the phone numbers in their advertising, marketing creative, and on their web properties with Invoca tracking numbers, and they must enable phone-based conversations to be recorded.
Who are your ideal customers in terms of company size and industries?
We primarily serve “considered purchases” industries in which a large proportion of our customers’ business (20-100%) transacts over the phone. Key verticals include healthcare, financial services, insurance, automotive, home services, travel and hospitality, retail/e-commerce, affiliate/performance marketing, and digital marketing agencies.
We specialize in serving mid-market and enterprise businesses that have high call volumes (more than 1,000 inbound calls/month) and high transaction value.
What do you see as the limitations of AI as it exists today?
AI is only as good as the data that is used to train it, and sourcing quality data is expensive and/or time consuming. Conversational data presents unique challenges because of the richness of the data. The data contains multiple speakers talking about different topics and with different sentiments all of which vary throughout the conversation. Conversations are naturally resistant to binary classifications that are typical in AI.
For example in image recognition in AI, a picture either contains an object or it does not. Determining if a conversation was “positive” or “negative” is far more subjective. Invoca has made strides using AI itself to assist with the training data curation process dramatically reducing the cost and effort required to train new AIs.
What do you see as the future potential of AI in marketing?
Marketers will continue to capitalize on AI to generate new insights and automate manual processes. Two trends that we predict will gain momentum are:
Unsupervised learning: By allowing machines to spot patterns, marketers can eliminate preconceived ideas and assumptions from their data analysis, resulting in the discovery of unexpected insights.
An example of this technology is our Signal Discovery technology. Unsupervised AI does not require a human to “supervise” the learning process (e.g., by providing training data with ground truth labels).
Accessibility: A major challenge marketers face when deploying AI is that the software often isn’t built with marketers in mind. To get value from the system, the user must have specific training in data science techniques to query and manipulate the data. We believe that software that puts the power of AI into the hands of the marketer will see significant growth in the coming years. This requires easy-to-understand data visualizations and easy-to-follow wizards that allow marketers to train their models themselves.
Any other thoughts on AI in marketing, or advice for marketers who are just starting with AI?
Your customer needs are changing rapidly, and your marketing should too. Choose a use case for AI that fixes a challenge in efficiency or agility that you are facing. Run tests with better data, show stakeholders the improved results and keep iterating. Now is the time to demonstrate your ability to pivot and optimize quickly, as well as better serve your customers.
Paul Roetzer is founder and CEO of Marketing AI Institute. He is the author of Marketing Artificial Intelligence (Matt Holt Books, 2022) The Marketing Performance Blueprint (Wiley, 2014) and The Marketing Agency Blueprint (Wiley, 2012); and creator of the Marketing AI Conference (MAICON).